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Title: Probabilistic Power Flow Based on a Gaussian Process Emulator

Abstract

In this letter, a novel Gaussian process emulator is proposed, for the first time, to conduct the probabilistic power-flow calculation. Based on Bayesian inference, a Gaussian process emulator is trained and served as a nonparametric, reduced-order model of the nonlinear power-flow model. This emulator has allowed us to evaluate the time-consuming power-flow solver at the sampled values with a negligible computational cost. The simulations reveal the excellent performance of this method.

Authors:
ORCiD logo [1];  [2]; ORCiD logo [1]; ORCiD logo [3]; ORCiD logo [3]
  1. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Falls Church, VA (United States)
  2. Univ. of California, Santa Cruz, CA (United States)
  3. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); USDOE Office of Electricity (OE); National Science Foundation (NSF)
OSTI Identifier:
1668501
Report Number(s):
LLNL-JRNL-790158
Journal ID: ISSN 0885-8950; 988765
Grant/Contract Number:  
AC52-07NA27344; 1917308
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Power Systems
Additional Journal Information:
Journal Volume: 35; Journal Issue: 4; Journal ID: ISSN 0885-8950
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; probabilistic power flow; Gaussian process emulator; Latin hypercube sampling; copula

Citation Formats

Xu, Yijun, Hu, Zhixiong, Mili, Lamine, Korkali, Mert, and Chen, Xiao. Probabilistic Power Flow Based on a Gaussian Process Emulator. United States: N. p., 2020. Web. doi:10.1109/tpwrs.2020.2983603.
Xu, Yijun, Hu, Zhixiong, Mili, Lamine, Korkali, Mert, & Chen, Xiao. Probabilistic Power Flow Based on a Gaussian Process Emulator. United States. https://doi.org/10.1109/tpwrs.2020.2983603
Xu, Yijun, Hu, Zhixiong, Mili, Lamine, Korkali, Mert, and Chen, Xiao. Wed . "Probabilistic Power Flow Based on a Gaussian Process Emulator". United States. https://doi.org/10.1109/tpwrs.2020.2983603. https://www.osti.gov/servlets/purl/1668501.
@article{osti_1668501,
title = {Probabilistic Power Flow Based on a Gaussian Process Emulator},
author = {Xu, Yijun and Hu, Zhixiong and Mili, Lamine and Korkali, Mert and Chen, Xiao},
abstractNote = {In this letter, a novel Gaussian process emulator is proposed, for the first time, to conduct the probabilistic power-flow calculation. Based on Bayesian inference, a Gaussian process emulator is trained and served as a nonparametric, reduced-order model of the nonlinear power-flow model. This emulator has allowed us to evaluate the time-consuming power-flow solver at the sampled values with a negligible computational cost. The simulations reveal the excellent performance of this method.},
doi = {10.1109/tpwrs.2020.2983603},
journal = {IEEE Transactions on Power Systems},
number = 4,
volume = 35,
place = {United States},
year = {Wed Apr 01 00:00:00 EDT 2020},
month = {Wed Apr 01 00:00:00 EDT 2020}
}